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AI For Beginners
Brian Pichman
Evolve Project
@Bpichman
Mastadon: https://libraryland.social

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AI: The Good, The Bad, The Ugly
This session explores what artificial intelligence (AI) is and the
everyday use cases for AI. It’s an introductory look at how various
industries, including libraries, use AI for operational efficiencies,
enhanced services, and more.
Today we are exploring…
Welcome

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What is Artificial Intelligence
the theory and development of computer systems able to perform tasks
that normally require human intelligence, such as visual perception,
speech recognition, decision-making, and translation between languages.

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What is Machine Learning
Machine Learning is a subset of Artificial Intelligence that uses algorithms
and statistical models to allow a computer system to make decisions
around a specific task without explicit instructions; relying on patterns and
inference

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When talking about AI…
People will have one of two
reactions:

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“The Agenda”
Topics for Today
AI As A Tool
When introducing AI to the its
best to explain what it is…and
what it isn’t.
Impacts to Industries
How are we seeing AI Impact
different industries now?
Pulling it Together
How can we embrace AI and
move things forward?
Using AI
Many of us interact regularly
with some variant of Artificial
Intelligence.

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AI is becoming more available to the masses; being incorporated into
our smart phones, our connected homes, and simple robots we
already use today such as Roomba. With more accessible cloud-
computing, open source, and the making community, this field will
rapidly expand.
More Widely Available
Using AI
“The development of full artificial
intelligence could spell the end of the
human race….It would take off on its own,
and re-design itself at an ever increasing
rate. Humans, who are limited by slow
biological evolution, couldn't compete, and
would be superseded.”— Stephen Hawking

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What makes up an intelligent system?
AI Components
Logic and Rules Based
Computer makes decisions
based on a decision tree,
logic rules, or a predefined
process with a calculated
result.
Pattern Based
(Machine Learning)
Computer learns overtime by
using data and algorithms to
detect patterns.
Deep Learning
Deep Learning is a subset of
Machine Learning that
enables the computer to
make decisions on its own.
Neural Networks
A neural network allows an
AI to make its own
conclusions, where a simple
pattern-only based AI must
rely solely on data. A neural
network allows deep learning
to function.

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Pattern Based Intelligence -> currently exists with self driving cars, language translations,
movie recommendations etc.
Strong Artificial Intelligence -> (doesn’t yet exist)
• computers think at a level that meets or passes people (abstract thinking)
Artificial Intelligence Exists

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Flash Light Examples
Understanding AI
If an ML algorithm makes an
inaccurate prediction, then the
engineer needs to correct. In DL,
the algorithms can determine on
their own if a prediction is accurate
or not.
Deep Learning
Allow machines to make to
their own accurate decisions
without intervention from
engineer
Neural Networks
If detects {dark} turn on {light}
Logic Rules
it’s performing a function with the
data given and gets progressively
better at that function
Machine Learning
Eventually, the system can
turn on the light with other
queues such as “I can’t see”
DL “Code”
Flashlight will turn on automatically
as it learns other words for “dark”
picking up on phrases that
contains the word
ML “Code”:

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create an algorithm that is able to teach itself without any external help
Pattern Recognition
Deep Learning
Uses more complicated mathematical models to define
pictures content and speech
Self Learning
The advance machine
learning system makes
decisions by analyzing its own
data and making patterns
Learning on Examples
This method is used when a
machine learns through
examples. For instance,
Google’s automatic spam
filtering learns as users report
spam.
Learning on Experience
The system learns from positive and negative
experiences.

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From Patterns to Automation
AI Models
The idea is that an algorithm will sift through the data, learn from it, and apply it to make a decision. This can be seen in any
recommendation type service. Machine Learning takes it a step farther by automating tasks; helping data security firms identify potential
threats or finance looking for favorable deals.
AI’s can be Transactional in which a question is asked and an answer is given, like a virtual assistant. AI’s can also be Automated in
which routine tasks such automatically taking trash out on garbage day.

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• When editing or using filters in photos (do X to eyes and Y to
ears)
• Identification of license plates from an image in a toll violation
• Facebook’s ability to identify and recommend faces in photos
• iPhone users can have their phone categorize people by facial
patterns – in which you then define their name
• Google’s Image Recognition
Examples
How we see AI In Everyday Life
Image Recognition
Think of how we can use facial imaging
to determine moods

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You probably see this everyday if you use Siri, Google Home, or an
Echo Product.
Overtime or with training, a system can tailored results based on
identifying the user asking. For example, Google Home will provide
my personal driving times to work if it hears me ask “how long will it
take me to get to work” versus a friend asking who it has no data on.
Examples
How we see AI In Everyday Life
Voice Recognition
Think of how a system can respond
and remember a user based solely
on their voice

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Helpful – AI

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And How We Use It
Other Forms of AI
Optical Character Recognition
Think of how a picture of your license plate
allows a machine to translate that to text and
run a query to determine who violated a toll.
Also see this in scanners that can take an
image and convert this to text.
Consider how you can take a photo of another
language and have it translate to yours
Advance User Preferences
This is the concept of an AI providing solutions
based on historic user’s preferences and
comparing it to similar users.
Compare how Amazon or Netflix makes
recommendations based on your purchases or
views – or even how Amazon guesses when
you might run out of a specific product.
Sensory Data Analysis
Your wearables that detect heart rate for
instance can determine without user
intervention if you are working out and even
what kind of work out such as jogging or
bicycling.

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Healthcare
Used in healthcare to identify and notice predictable
trends – such as having a machine look at charts to
recognize tumors sooner with more accuracy – or
eyes to determine stage of glaucoma

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Good Read:
https://www.businessinsider.com/healthcare-artificial-intelligence-pitfalls-2019-3

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Smart Homes
See how a home can alert when it sees a person
versus an animal or know that its going to rain
tomorrow so no need to water the grass today

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Service Industries
AI has the potential to replace many minimum wage
jobs

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https://www.bbc.com/news/av/technology-43292047/burger-flipping-robot-begins-first-shift

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Autonomous Driving
Autonomous driving Level 4 describes vehicles that can operate without human interaction in most, but not all, conditions and locations and will likely operate in geofenced areas.
Autonomous driving Level 5 labels vehicles operating autonomously in all situations and conditions, and controlling all tasks.

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Inspiring AI’s
AI: AlphaGo
AlphaGo is the first AI to beat a
human in arguably the most difficult
game to master. AlphaGo now
teaches moves to trainees.
AI: ROSS
ROSS is an AI tool to make legal
research easier and faster

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Run-away AI’s
Tay (Thinking About You)
Released on March 23 2016 via Twitter, Tay (as
TayTweets on Twitter) was designed to mimic the
interactions of 19 year old girl through learned
conversations on Twitter.
Users began tweeting pollitcally incorrect phrases to Tay,
and thus, Tay responded and answered with the learned
inappropriate behavior – as it was it was not taught what
the difference between Good Language and Bad
Language was.
Microsoft Artificial Chatter Bot

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Run-away AI’s
Inspirobot.me
I am an artificial intelligence dedicated to generating
unlimited amounts of unique inspirational quotes for
endless enrichment of pointless human existence.
-- From their website
Happy Accidents

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Logic and Rules Based
Challenges for AI
Training
Similar to having good data, an AI
might need to learn the correct
response for the correct situation or
identify dangers or inappropriate
interactions
Precision
The idea of garbage data in
garbage data out. If you flood
an AI with bad data and don’t
set the proper syntax or
thresholds you will get
incoherent results
Context
AI’s can struggle with understanding context. For
example, asking Siri ”call me an ambulance” may yield
“OK, from now on, I will call you Ambulance”

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Things to Expand Your Knowledge
Cool Resources to Check Out
IBM Watson
Watson was created as a question answering (QA) computing system that
IBM built to apply advanced natural language processing, information
retrieval, knowledge representation, automated reasoning, and machine
learning technologies to the field of open domain question answering. –
Wikipedia
Powered by the latest innovations in machine learning, Watson lets you learn more
with less data. You can integrate AI into your most important business processes,
informed by IBM’s rich industry expertise. You can build models from scratch, or
leverage our APIs and pre-trained business solutions. No matter how you use
Watson, your data and insights belong to you − and only you.
--IBM Watson

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By Pgr94 - Own work based on diagram found at
http://www.aaai.org/Magazine/Watson/watson.php, CC0,
https://commons.wikimedia.org/w/index.php?curid=14575947

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Things to Expand Your Knowledge
Cool Resources to Check Out
Kaggle
Kaggle is an online community of data scientists and machine learners,
owned by Google, Inc. Kaggle allows users to find and publish data sets,
explore and build models in a web-based data-science environment, work
with other data scientists and machine learning engineers, and enter
competitions to solve data science challenges. Kaggle got its start by
offering machine learning competitions and now also offers a public data
platform, a cloud-based workbench for data science, and short form AI
education. -- Wikipedia

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Things to Expand Your Knowledge
Cool Resources to Check Out
TensorFlow
TensorFlow is an open-source software library for dataflow
programming across a range of tasks. It is a symbolic math library,
and is also used for machine learning applications such as neural
networks. It is used for both research and production at Google.
TensorFlow was developed by the Google Brain team for internal
Google use. It was released under the Apache 2.0 open-source
license on November 9, 2015. -- Wikipedia

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Books written by an AI
https://link.springer.com/book/10.1007/978-3-030-16800-1

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AI Games
AI Image or Not: http://www.whichfaceisreal.com/
https://experiments.withgoogle.com/collection/ai

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AI Content Creation Tools
Learn more about these in the other sessions today!

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Activity
https://affinelayer.com/pixsrv/

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Feel free to reach out!
Questions / Contacts
815-534-0403
www.evolveproject.org
bpichman@evolveproject.org
Twitter: @bpichman
linkedin.com/in/bpichman
slideshare.net/bpichman